Please use this identifier to cite or link to this item:
https://dair.nps.edu/handle/123456789/4159
Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Kory Krebs | - |
dc.date.accessioned | 2020-12-01T19:51:32Z | - |
dc.date.available | 2020-12-01T19:51:32Z | - |
dc.date.issued | 2020-08-20 | - |
dc.identifier.citation | Published--Unlimited Distribution | en_US |
dc.identifier.uri | https://dair.nps.edu/handle/123456789/4159 | - |
dc.description | Contract Management / Graduate Student Research | en_US |
dc.description.abstract | The U.S. Department of Defense has issued a policy directing agencies to adopt artificial-intelligence capabilities to increase the speed and agility of operations. Despite this, there are no current initiatives to adopt artificial intelligence technologies into the Defense Acquisition System. This thesis examines the potential impact of artificial intelligence on DoD procurement. It studies organizations that have incorporated the technology in business practices, and analyzes how well they improved speed and agility based on interviews with key personnel. While the interviewees were not always clear about what artificial intelligence is, the technology could be helpful for procurement in market research, negotiations, insight, and contract management, among other ways. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | Acquisition Research Program | en_US |
dc.relation.ispartofseries | Artificial-Intelligence Capabilities;NPS-CM-20-152 | - |
dc.subject | Acquisition|| | en_US |
dc.subject | Contracting | en_US |
dc.subject | Procurement | en_US |
dc.subject | Artificial Intelligence | en_US |
dc.subject | Innovation | en_US |
dc.subject | Automation | en_US |
dc.subject | Emerging Technologies | en_US |
dc.title | How can the Dod Adopt Commercial-Style Artificial Intelligence for Procurement? | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | NPS Graduate Student Theses & Reports |
Files in This Item:
File | Description | Size | Format | |
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NPS-CM-20-152.pdf | Student Paper | 1.23 MB | Adobe PDF | View/Open |
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